• Title/Summary/Keyword: Traffic Classification

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Effects of Vehicle Classification Methods on Noise Prediction Results of Road Traffic Noise Map (소음지도 제작시 차량 분류방법이 소음도 예측 결과에 미치는 영향 연구)

  • Kim, Ji-Yoon;Park, In-Sun;Jung, Woo-Hong;Kang, Dae-Joon;Park, Sang-Kyu
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.22 no.2
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    • pp.193-197
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    • 2012
  • Road traffic noise map is effective method to save cost and time for environmental noise assessment. Generally, noise is calculated by using theoretical equation of noise prediction, and the calculated result can be influenced by various input factors. Especially, domestic vehicle classification method for traffic flow and heavy vehicle percentage is different from that of foreign countries. Thus, this can cause effect on the noise prediction results. In this study, noise prediction results by using domestic vehicle classification method are compared with those by foreign methods.

Classification of Tor network traffic using CNN (CNN을 활용한 Tor 네트워크 트래픽 분류)

  • Lim, Hyeong Seok;Lee, Soo Jin
    • Convergence Security Journal
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    • v.21 no.3
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    • pp.31-38
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    • 2021
  • Tor, known as Onion Router, guarantees strong anonymity. For this reason, Tor is actively used not only for criminal activities but also for hacking attempts such as rapid port scan and the ex-filtration of stolen credentials. Therefore, fast and accurate detection of Tor traffic is critical to prevent the crime attempts in advance and secure the organization's information system. This paper proposes a novel classification model that can detect Tor traffic and classify the traffic types based on CNN(Convolutional Neural Network). We use UNB Tor 2016 Dataset to evaluate the performance of our model. The experimental results show that the accuracy is 99.98% and 97.27% in binary classification and multiclass classification respectively.

AutoML and CNN-based Soft-voting Ensemble Classification Model For Road Traffic Emerging Risk Detection (도로교통 이머징 리스크 탐지를 위한 AutoML과 CNN 기반 소프트 보팅 앙상블 분류 모델)

  • Jeon, Byeong-Uk;Kang, Ji-Soo;Chung, Kyungyong
    • Journal of Convergence for Information Technology
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    • v.11 no.7
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    • pp.14-20
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    • 2021
  • Most accidents caused by road icing in winter lead to major accidents. Because it is difficult for the driver to detect the road icing in advance. In this work, we study how to accurately detect road traffic emerging risk using AutoML and CNN's ensemble model that use both structured and unstructured data. We train CNN-based road traffic emerging risk classification model using images that are unstructured data and AutoML-based road traffic emerging risk classification model using weather data that is structured data, respectively. After that the ensemble model is designed to complement the CNN-based classification model by inputting probability values derived from of each models. Through this, improves road traffic emerging risk classification performance and alerts drivers more accurately and quickly to enable safe driving.

Video Quality Representation Classification of Encrypted HTTP Adaptive Video Streaming

  • Dubin, Ran;Hadar, Ofer;Dvir, Amit;Pele, Ofir
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.8
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    • pp.3804-3819
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    • 2018
  • The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet inspection (DPI). However, Google and certain content providers have started to encrypt their video services. As a result, operators often encounter difficulties in shaping their encrypted video traffic via DPI. This highlights the need for new traffic classification methods for encrypted HTTP adaptive video streaming to enable smart traffic shaping. These new methods will have to effectively estimate the quality representation layer and playout buffer. We present a new machine learning method and show for the first time that video quality representation classification for (YouTube) encrypted HTTP adaptive streaming is possible. The crawler codes and the datasets are provided in [43,44,51]. An extensive empirical evaluation shows that our method is able to independently classify every video segment into one of the quality representation layers with 97% accuracy if the browser is Safari with a Flash Player and 77% accuracy if the browser is Chrome, Explorer, Firefox or Safari with an HTML5 player.

An Efficient Online RTP Packet Classification Method for Traffic Management In the Internet (인터넷상에서 트래픽 관리를 위한 효율적인 RTP 패킷 분류 방법)

  • Roh Byeong-hee
    • Journal of Internet Computing and Services
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    • v.5 no.5
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    • pp.39-48
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    • 2004
  • For transporting real-time multimedia traffic, RTP is considered as one of the most promising protocols operating at application layer. In order to manage and control the real-time multimedia traffic within networks, network managers need to monitor and analyze the traffic delivering through their networks. However, conventional traffic analyzing tools can not exactly classify and analyze the real-time multimedia traffic using RTP on the basis of real-time as well as non-real-time operations. In this paper, we propose an efficient online classification method of RTP packets, which can be used on high-speed network links. The accuracy and efficiency of the proposed methodhave been tested using captured data from a KIX node with 100 Mbps links, which interconnects between domestic and overseas Internet networks and is operated by NCA.

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Classification Method of Congestion Change Type for Efficient Traffic Management (효율적인 교통관리를 위한 혼잡상황변화 유형 분류기법 개발)

  • Shim, Sangwoo;Lee, Hwanpil;Lee, Kyujin;Choi, Keechoo
    • International Journal of Highway Engineering
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    • v.16 no.4
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    • pp.127-134
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    • 2014
  • PURPOSES : To operate more efficient traffic management system, it is utmost important to detect the change in congestion level on a freeway segment rapidly and reliably. This study aims to develop classification method of congestion change type. METHODS: This research proposes two classification methods to capture the change of the congestion level on freeway segments using the dedicated short range communication (DSRC) data and the vehicle detection system (VDS) data. For developing the classification methods, the decision tree models were employed in which the independent variable is the change in congestion level and the covariates are the DSRC and VDS data collected from the freeway segments in Korea. RESULTS : The comparison results show that the decision tree model with DSRC data are better than the decision tree model with VDS data. Specifically, the decision tree model using DSRC data with better fits show approximately 95% accuracies. CONCLUSIONS : It is expected that the congestion change type classified using the decision tree models could play an important role in future freeway traffic management strategy.

Research on the Efficiency of Classification of Traffic Signs Using Transfer Learning (전수 학습을 이용한 도로교통표지 데이터 분류 효율성 향상 연구)

  • Kim, June Seok;Hong, Il Young
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.37 no.3
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    • pp.119-127
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    • 2019
  • In this study, we investigated the application of deep learning to the manufacturing process of traffic and road signs which are constituting the road layer in map production with 1 / 1,000 digital topographic map. Automated classification of road traffic sign images was carried out through construction of training data for images acquired by using transfer learning which is used in image classification of deep learning. As a result of the analysis, the signs of attention, regulation, direction and assistance were irregular due to various factors such as the quality of the photographed images and sign shape, but in the case of the guide sign, the accuracy was higher than 97%. In the digital mapping, it is expected that the automatic image classification method using transfer learning will increase the utilization in data acquisition and classification of various layers including traffic safety signs.

Development of Traffic Accident Models in Seoul Considering Land Use Characteristics (토지이용특성을 고려한 서울시 교통사고 발생 모형 개발)

  • Lim, Samjin;Park, Juntae
    • Journal of the Society of Disaster Information
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    • v.9 no.1
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    • pp.30-49
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    • 2013
  • In this research we developed a new traffic accident forecasting model on the basis of land use. A new traffic accident forecasting model by type was developed based on market segmentation and further introduction of variables that may reflect characteristics of various regions using Classification and Regression Tree Method. From the results of analysis, activities variables such as the registered population, commuters as well as road size, traffic accidents causing facilities being the subjects of activities were derived as variables explaining traffic accidents.

Classification of Network Traffic using Machine Learning for Software Defined Networks

  • Muhammad Shahzad Haroon;Husnain Mansoor
    • International Journal of Computer Science & Network Security
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    • v.23 no.12
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    • pp.91-100
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    • 2023
  • As SDN devices and systems hit the market, security in SDN must be raised on the agenda. SDN has become an interesting area in both academics and industry. SDN promises many benefits which attract many IT managers and Leading IT companies which motivates them to switch to SDN. Over the last three decades, network attacks becoming more sophisticated and complex to detect. The goal is to study how traffic information can be extracted from an SDN controller and open virtual switches (OVS) using SDN mechanisms. The testbed environment is created using the RYU controller and Mininet. The extracted information is further used to detect these attacks efficiently using a machine learning approach. To use the Machine learning approach, a dataset is required. Currently, a public SDN based dataset is not available. In this paper, SDN based dataset is created which include legitimate and non-legitimate traffic. Classification is divided into two categories: binary and multiclass classification. Traffic has been classified with or without dimension reduction techniques like PCA and LDA. Our approach provides 98.58% of accuracy using a random forest algorithm.

A Study of Classification of Road Tunnel for Fire Safety (안전성 향상을 위한 도로터널 등급에 관한 연구)

  • Yoo, Ji-Oh;Rie, Dong-Ho;Shin, Hyun-Jun
    • Journal of the Korean Society of Safety
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    • v.20 no.3 s.71
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    • pp.112-119
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    • 2005
  • In road tunnel, in order to prevents an accident and minimize the damage of an accident in the case of fire, safety facilities and equipments are integral parts. The type and amount of safety facilities are based on tunnel type and length, traffic flow rate, etc. Therefore many countries use a tunnel classification system that categories tunnel into groups, and specifies the necessary emergency equipment for each group. In this study, for the purpose of classifying tunnel based on tunnel ist investigated the domestic and foreign standards and regulations for safety of road tunnel. As a results, we suggest the method of classification of tunnel by traffic performance, tunnel grade, the volume of traffic, fraction of HGV, rules or regulations for transports of dangerous good through tunnel.